Multivariate statistical process control of batch processes based on three-way models

Louwerse, D.J.; Smilde, Age K.

doi:10.1016/s0009-2509(99)00408-x

Cited by 157 publications

(125 citation statements)

References 17 publications

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“…The suggested method, however, is restricted to (pseudo) first-order kinetic models, since concentration profiles have to be written as a sum of exponentially decaying terms and author's goal was only to estimate accurately kinetic parameters. There are also some reports on the use of hard constraint in restricted Tucker3 models [26][27][28][29]. However, in our knowledge, there are no reports on applying hard modeling on some concentration profiles to reduce rotational ambiguity in the PARAFAC solution.…”

Section: Research Articlementioning

confidence: 99%

Hard–soft modeling parallel factor analysis to solve equilibrium processes

Sajjadi

Abdollahi

2011

Journal of Chemometrics

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PARAFAC model is the most famous model for analyzing three-way data. However, this method does not converge to chemically meaningful solutions when applied to three-way problems involving rank overlap profiles at least in one mode. Rank overlap can be simply found where components have similar spectral profiles or analytes appearing in identical proportions throughout an experiment. However, an appropriate selection of the initial parameters and constraints such as non-negativity and unimodality can still make PARAFAC model useful in this regard. Although such constraints reduce rotational freedom in PARAFAC solution, they are generally insufficient to wholly eliminate the rotational problem. The goal of the present paper is to incorporate hard modeling constraint in the soft-modeled PARAFAC algorithm to overcome non-uniqueness problem in the equilibrium processes involving linearly dependent factors at least in one mode. The hard constraint is introduced to force some or all of the concentration profiles to fulfill an equilibrium model that is refined at each iteration cycle of the optimization process of PARAFAC. The proposed approach is called hard-soft PARAFAC (HSPARAFAC). When the rank overlap species obeys equilibrium model in HSPARAFAC, the unique results are obtained even in the presence of non-modeled interferences. The new modification in the treatment of equilibrium data sets yields more satisfactory results than the exclusive PARAFAC algorithm. Simulated and real examples with rank overlap problem are used to confirm this statement.

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Section: Research Articlementioning

confidence: 99%

Hard–soft modeling parallel factor analysis to solve equilibrium processes

Sajjadi

Abdollahi

2011

Journal of Chemometrics

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“…This reveals the multiperiod characteristics of the process and improves the real-time performance. Several mark points are selected at equal intervals over one operation cycle; 13 from the beginning of production to each mark point represents one suboperation period. Models are established for each time period to reduce the number of estimated samples.…”

Section: Introductionmentioning

confidence: 99%

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

Wen

Zhou

et al. 2017

International Journal of Advanced Robotic Systems

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As an important production method, the batch process is complex and flexible. Moreover, the modeling complexity and the spatial complexity of the storage model are higher, and the monitoring of the actual batch process is more difficult. To address this problem, this article proposes a fault detection method based on random projection, K-means clustering, and the k-nearest neighbor algorithm. First, a multiperiod division method is put forward based on the random projection and the K-means clustering algorithm. This reduces the computational complexity while ensuring the fault detection performance of the algorithm. Second, a real-time monitoring model is established based on each sub-period data using the k-nearest neighbor method to realize online monitoring of the batch production process. According to the premise that the fault detection performance is approximately equal, the proposed method reduces the complexity and computation of the model and realizes the real-time demand of fault detection.

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“…In order to overcome the need for estimating the unknown part of the process variable trajectory deviations from the current time until the end of the batch, Rannar et al [6] proposed adaptive batch monitoring using hierarchical PCA. Louwerse and Smilde [7] proposed the correction of the unfold-PCA approach, which removed the necessity of using a too high limit for the Q-chart. Chen and Liu [8] developed the Dynamic PCA (DPCA) and PLS models to be used in on-line batch process monitoring, and implemented it in three case studies through simulation.…”

Section: Introductionmentioning

confidence: 99%

On‐line Statistical Process Monitoring and Fault Diagnosis in Batch Baker's Yeast Fermentation

et al. 2009

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This study involves real-time monitoring and fault diagnosis in batch baker's yeast fermentation. A specific Real Time Statistical Process Analysis and Control (RT-SPAC) program was developed to monitor instantaneous reaction conditions. The air flow rate fed to the reactor, temperature, pH, and dissolved oxygen concentration in a laboratory-size fermenter were monitored and recorded by means of on-line sensors. Under control of the RT-SPAC program, 22 batch baker's yeast fermentation operations were carried out. In the first 20 operations, an ordinary process was followed under previously defined nominal operating conditions. Historical data collected from these batches were then used for online Dynamic Principal Component Analysis (DPCA) in the course of the following two batches. The last two batches were implemented such that some deliberate faults (in temperature and pH) were introduced during the operation. The results indicated that the software was capable of capturing the process faults, and furthermore the possible causes of these faults were identified by contribution plots.

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Multivariate statistical process control of batch processes based on three-way models

Cited by 157 publications

References 17 publications

Hard–soft modeling parallel factor analysis to solve equilibrium processes

Hard–soft modeling parallel factor analysis to solve equilibrium processes

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

On‐line Statistical Process Monitoring and Fault Diagnosis in Batch Baker's Yeast Fermentation

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